Methods and systems for using a cloud computing environment to share biological related data

ABSTRACT

The present disclosure provides a novel approach for shifting or distributing various information (e.g., protocols, analysis methods, sample preparation data, sequencing data, etc.) to a cloud-based network. For example, the techniques relate to a cloud computing environment configured to receive this information from one or more individual sample preparation devices, sequencing devices, and/or computing systems. In turn, the cloud computing environment may generate information for use in the cloud computing environment and/or to provide the generated information to the devices to guide a genomic analysis workflow. Further, the cloud computing environment may be used to facilitate the sharing of sample preparation protocols for use with generic sample preparation cartridges and/or monitoring the popularity of the sample preparation protocols.

This application is a non-provisional of Provisional Patent ApplicationNo. 61/756,547, entitled “METHODS AND SYSTEMS FOR USING A CLOUDCOMPUTING ENVIRONMENT TO SHARE BIOLOGICAL RELATED DATA”, filed Jan. 25,2013, which is herein incorporated by reference in its entirety for allpurposes.

BACKGROUND

The present disclosure relates generally to the field of data gatheringand analysis related to biological samples. More particularly, thedisclosure relates to techniques for interacting with a cloud computingenvironment to share, store, and analyze biological related information(e.g., biological data, protocols, analysis methods, etc.).

Genetic sequencing has become an increasingly important area of geneticresearch, promising future uses in diagnostic and other applications. Ingeneral, genetic sequencing involves determining the order ofnucleotides for a nucleic acid such as a fragment of RNA or DNA.Relatively short sequences are typically analyzed, and the resultingsequence information may be used in various bioinformatics methods tologically fit fragments together to reliably determine the sequence ofmuch more extensive lengths of genetic material from which the fragmentswere derived. Automated, computer-based examinations of characteristicfragments have been developed and have been used more recently in genomemapping, identification of genes and their function, and so forth.However, existing techniques are highly time-intensive, and resultinggenomic information is accordingly extremely costly.

A number of alternative sequencing techniques are presently underinvestigation and development. In several techniques, typically singlenucleotides or strands of nucleotides (oligonucleotides) are introducedand permitted or encouraged to bind to the template of genetic materialto be sequenced. Sequence information may then be gathered by imagingthe sites. In certain current techniques, for example, each nucleotidetype is tagged with a fluorescent tag or dye that permits analysis ofthe nucleotide attached at a particular site to be determined byanalysis of image data. Although such techniques show promise forsignificantly improving throughput and reducing the cost of sequencing,further progress in speed, reliability, and efficiency of data handlingis needed.

For example, in certain sequencing approaches that use image data toevaluate individual sites, large volumes of image data may be producedduring sequential cycles of sequencing. In systems relying uponsequencing by synthesis (SBS), for example, dozens of cycles may beemployed for sequentially attaching nucleotides to individual sites.Images formed at each step result in a vast quantity of digital datarepresentative of pixels in high-resolution images. These images areanalyzed to determine what nucleotides have been added to each site ateach cycle of the process. Other images may be employed to verifyde-blocking and similar steps in the operations.

In many sequencing approaches the image data is important fordetermining the proper sequence data for each individual site. While theimage data may be discarded once the individual nucleotides in asequence are identified, certain information about the images, such asinformation related to image or fluorescence quality, may be maintainedto allow researchers to confirm base identification or calling. Theimage quality data in combination with the base identities for theindividual fragments that make up a genome will become unwieldy assystems become capable of more rapid and large-scale sequencing. Thereis need, therefore, for improved techniques in the management of suchdata during and after the sequencing process.

Besides the data gathered during and after sequencing, the genomicanalysis workflow from sample extraction to reporting of the dataanalysis may involve the generation of a significant amount ofpaper-based information such as lab tracking forms, user guides, andvarious manifests for tracking sample and content information. All ofthe paper-based information may complicate the genomic analysis workflowfor both individuals and larger entities performing genomic analysis.Thus, there is a need for improved techniques in the management of suchinformation before, during, and after the genomic analysis workflow.

Further, certain steps within the genomic analysis workflow may besubject to a great deal of variability due to different individuals andentities performing the steps. For example, sample preparation includesa high degree of diversity (e.g., in number of steps, processing time,and specific chemistry needed for specific genomic analysisapplications). Also, sample preparation has historically been the leastautomated and integrated part of the genomic analysis workflow, whileincluding the highest user-to-user and site-to-site variability. Thus,there is a need for improved techniques to create a more tightlyintegrated workflow from sample extraction to reporting, while makingthe genomic analysis workflow more accessible to individuals and largerentities and promoting sharing between these individuals and entities.

Yet further, certain sample preparation cartridges used in preparingsamples for genomic analysis (e.g., the sequencing described above) maynot serve the specific needs (e.g., specific application) of the user.Additionally, individuals or entities with lower-throughput needs andlacking resources may not utilize an automated sample preparation systemand/or application-specific sample preparation cartridges, but insteadutilize self-derived assays. Thus, there is a need for providing acustomizable sample preparation system for use with an automated samplepreparation system by those individuals or entities withlower-throughput needs and or lacking resources.

BRIEF DESCRIPTION

The present disclosure provides a novel approach for shifting ordistributing various information (e.g., protocols, analysis methods,sample preparation data, sequencing data, etc.) to a cloud-based network(e.g., a local cloud or a remote cloud). For example, the techniquesrelate to a cloud computing environment configured to receive thisinformation from one or more individual sample preparation devices,sequencing devices, and/or computing systems. In particular embodiments,the information may be stored and/or analyzed using the cloud computingenvironment, which may reduce the processing and/or storage burdenassociated with the instrument itself or an associated computer.Instruments such as sample preparation devices and sequencing devicesrepresent significant capital investments for researchers, and areduction in processing burden may result in a decreased cost per run.Further, because various steps in a genomic workflow analysis may beconducted at core laboratory facilities, the owner of the informationmay not be local to the instrument. Storage of information in a cloudcomputing environment as provided herein allows location-independentaccess and storage, as well as backup storage. Accordingly, highthroughput facilities as well as smaller labs may have reduced memoryrequirements on-site for storing client data.

The cloud computing environment may also provide sharing of protocols,analysis methods, libraries, sequence data as well as distributedprocessing for sequencing, analysis, and reporting. The availability ofthis information through the cloud computing environment may promote atightly integrated workflow from sample extraction to reporting ofanalysis data in an application-centric fashion. In particular, duringthe physical genomic analysis process, the cloud computing environmentand the information stored therein may serve as a workflow manager thatchanges how the user selects an application (e.g., sample preparationapplication) and how the user interacts with the information availableor generated via the cloud computing environment.

In addition, the sharing and distributed processing also allowscomputing resources to be allocated (e.g., crowd-sourced) to particularprojects or users within the cloud computing environment. Such animplementation may allow small labs or small clients to accessinformation and an advanced data processing platform on a scale that isotherwise exclusive to larger labs by providing access at relativelylower costs, for example, on a pay-as-you-go basis. Alternatively oradditionally, such an implementation can provide a convenient venue orportal for purchasing a product from a supplier of a component of thegenomic analysis workflow (e.g., sample preparation cartridge). Thecloud computing environment may also facilitate a virtual plug and playinteraction between sample preparation devices, sequencing devices, anddata analysis platforms. That is, communication of the samplepreparation device and sequencing device and the cloud computingenvironment is relatively seamless and may be implemented without agreat deal of IT support. Researchers may relinquish responsibility forservicing and updating devices running dedicated programs for analyzingsequence data, because maintenance of the data analysis software isconducted via the cloud monitoring systems. Such an arrangement frees upIT resources at the user or client site.

The cloud computing environment may also promote the development andsharing of customizable sample preparation protocols for use withautomated sample preparation systems. For example, users may purchase ageneric sample preparation cartridge from a supplier (e.g., manufactureror provider). The generic sample preparation cartridge can be used, forexample, to convert nucleic acid samples (e.g., DNA or RNA) intolibraries for sequencing (e.g., massive parallel sequencing). Forexample, the libraries may be utilized in whole-genome sequencing,targeted resequencing, or any other genomic analysis with specializedpurposes. Based on the purpose for the sample preparation, the userdevelops a customized protocol for use with the generic samplepreparation cartridge. The sample preparation protocol may be used todrive the sample preparation instrument to perform each of the requiredsteps (e.g., mixing, incubation, splitting of samples and reagents,etc.) for a predetermined amount of time and at a specific temperature.The sample preparation protocol (e.g., optimized protocol) and/or acorresponding analysis method may be submitted to the cloud computingenvironment for other users to use. In addition, the cloud computingenvironment enables the use of a particular protocol (e.g., byrequesters or citations in publications), the rating of the protocol,and certification of the protocol. Indeed, application-specificcartridges may be developed by the supplier of the generic samplepreparation cartridge based in part on the reception of the submittedprotocol. To further promote the development and sharing of protocolsfor the generic sample preparation cartridges, the submitter of theprotocol may be credited with credit to purchase consumables from thesupplier. Thus, the cloud computing environment provides a platform forthe sharing and development of sample preparation protocols and/oranalysis methods for use with the generic sample preparation cartridge.

The present disclosure provides a computer-implemented method forsharing and monitoring use of protocols for preparing biological samplesusing generic sample preparation cartridges in a cloud computingenvironment. The method can include receiving from a submitter, at aserver, a protocol for sample preparation using a generic samplepreparation cartridge on the cloud computing environment. The method canalso include monitoring for a request from a requester for the protocolor for a use of the protocol. The method can further include creditingthe submitter with credit for purchasing consumables from a supplier ofthe generic sample preparation cartridge for at least one request forthe protocol or use of the protocol.

The present disclosure also provides a system for sharing and monitoringuse of protocols for preparing biological samples using generic samplepreparation cartridges. The system can include a cloud computingenvironment in communication with multiple computer systems. The cloudcomputing environment can include at least one server and at least oneprocessor. The at least one server can be configured to communicate withat least one of the computer systems to receive and store a protocol forsample preparation using a generic sample preparation cartridge. The atleast one processor can be configured to monitor for a request by arequester for the protocol and to credit a submitter of the protocolwith credit for purchasing consumables from a supplier of the genericsample preparation cartridge for each request for the protocol.

The present disclosure further provides a system for sharing andmonitoring use of protocols for preparing biological samples usinggeneric sample preparation cartridges that can include a cloud-basedserver in communication with multiple computer systems. The system canalso include a memory component that receives, via the server, protocolsfor sample preparation using generic sample preparation cartridges andstores the protocols. The system can further include a processorconfigured to receive requests for one or more protocols, monitor anumber of requests or uses for each of the protocols, and credit asubmitter of a respective protocol with credit for purchasingconsumables from a supplier of the generic sample preparation cartridgesfor each request for the respective protocol or use of the respectiveprotocol.

The present disclosure still further provides a computer-implementedmethod for analyzing biological samples in a cloud computingenvironment. The method can include receiving, at a server, sampleextraction related data and generating, via a processor a sampleextraction log based at least on the sample extraction related data. Themethod can also include receiving, at the server, sample preparationrelated data and generating, via the processor, a sample preparation logbased at least on the sample preparation related data and the sampleextraction log. The method can further include receiving, at the server,sequencing related data and generating, via the processor, a run logbased at least on the sample extraction log and the sequencing relateddata.

The present disclosure yet further provides a system for analyzingbiological samples. The system can include a cloud computing environmentin communication with multiple sample preparation devices, multiplesequencing devices, and multiple computing devices. The cloud computingenvironment can include at least one server. The at least one server canbe configured to communicate with at least one of the sample preparationdevices, at least one of the computing devices, and at least one of thecomputing devices remote from the at least one server to receive andstore sample preparation data from the at least one sample preparationdevice and sequence data from the at least one sequencing device whilethe sample preparation data and the sequence data are being generated.

Embodiments of the present techniques are described herein by referenceto sample preparation data generated by a sample preparation device,sequencing data generated by a sequencing device, and/or informationrelated to generating, analyzing, and reporting this type of data. Thedisclosure is not, however, limited by the advantages of theaforementioned embodiment. The present techniques may alternatively oradditionally be applied to devices capable of generating other types ofhigh throughput biological data, such as microarray data. Microarraydata may be in the form of expression data, and the expression data maybe stored, processed, and/or accessed by primary or secondary users inconjunction with the cloud computing environment as provided herein.Other devices that can be used include, but are not limited to, thosecapable of generating biological data pertaining to enzyme activity(e.g. enzyme kinetics), receptor-ligand binding (e.g. antibody bindingto epitopes or receptor binding to drug candidates), protein bindinginteractions (e.g. binding of regulatory components to nucleic acidenzymes), or cell activity (e.g. cell binding or cell activity assays).

DRAWINGS

FIG. 1 is a diagrammatical overview for a system incorporating a cloudcomputing environment in accordance with the present disclosure;

FIG. 2 is a diagrammatical overview of an individual node of the cloudcomputing environment of the type discussed with reference to FIG. 1;

FIG. 3 is a diagrammatical overview of a sequencing device that may beused in conjunction with the cloud computing environment of the typediscussed with reference to FIG. 1;

FIG. 4 is a diagrammatical overview of a sample preparation device thatmay be used in conjunction with the cloud computing environment of thetype discussed with reference to FIG. 1;

FIG. 5 is a schematic overview of a cloud-based computing environmentthat enables sample preparation protocol sharing and popularitymonitoring;

FIG. 6 is a flow diagram of a method of interaction of submitters,requesters, and a supplier with respect to the sharing and monitoring ofthe sample preparation protocol on the cloud-based computing environmentof the type discussed with reference to FIGS. 1 and 5;

FIG. 7 is a schematic overview of a cloud-based computing environment tofacilitate a cloud-guided genomic analysis workflow; and

FIG. 8 is a schematic overview of a flow diagram of a method ofinteraction of a user and instruments with the cloud-based computingenvironment of the type discussed with reference to FIGS. 1 and 7.

DETAILED DESCRIPTION

As used herein, the term “protocol” refers to a method, step orinstruction or set of methods, steps or instructions performed incompleting a task, such as preparing a biological sample. A samplepreparation protocol typically includes, for example, a step-by-step setof instructions to complete a task. The protocol may contain only asub-set of the steps needed to complete the task. The set ofinstructions can be performed entirely in a manual manner, entirely inan automated manner, or a mixture of one or more manual and automatedsteps may be performed in combination. For example, a sample preparationprotocol may have as an initial step the manual introduction of anucleic acid sample or cell lysate into an inlet port of a samplepreparation cartridge, after which the rest of the protocol is performedin an automated manner by a device.

As used herein, the term “sample preparation” refers to ways in which asample is processed. In typical embodiments, sample preparation occursprior to analysis of the sample. However, sample preparation may occurprior to, during, or after performance of one or more analyses of thesample. In some embodiments, sample preparation may include, but is notlimited to, one or more of isolating, purifying, separating, orcombining samples. The isolating, purifying, separating or combining maybe partial or some percentage up to full isolation, purification,separation or combination. In some embodiments, sample preparation mayinclude, but is not limited to, cleaving, degrading, annealing,hybridizing, denaturing, ligating, and other samples to process asample. Any suitable sample preparation technique as known in the artmay be used in the protocols, methods and devices presented herein, asexemplified by methods set forth in Maniatis et al., Molecular Cloning:A Laboratory Manual, 2d Edition, 1989, and Short Protocols in MolecularBiology, ed. Ausubel, et al, hereby incorporated by reference.

As used herein, the term “sample preparation cartridge” refers to adevice which can hold a sample and reagents, and which provides one ormore chambers for sample preparation. The term “generic cartridge”refers to a sample preparation cartridge which is not limited to any oneparticular protocol. For example, in some embodiments, a generic samplepreparation cartridge may not include any reagents, and reagents areadded to the cartridge as needed by the user. In other embodiments, ageneric cartridge may include specific reagents, compartments andconnections required for and dedicated to a specific application (e.g.,whole transcriptome sample preparation). The use of the genericcartridge enables a user to utilize their own customized protocol foruse with the cartridge to address the specific need or application ofthe user.

As used herein, the term “publication” refers to a document, which maybe a hard copy or may be electronic, such as an online document. In someembodiments, the number of publications that cite, use, or both cite anduse a protocol can be useful for determining the status of the protocol.In some embodiments, the publication is a printed publication. In someembodiments, the publications are industry-specific journal articles,technical notes or some other form of peer-reviewed document. In someembodiments, the publications are textbooks, compilations of protocols,web logs, or other documents where a particular protocol is noted,followed and/or discussed by the authors.

As used herein, the term “certified status” refers to a designation thatcan be conferred to a protocol when one or more criteria have been met.For example, a protocol can achieve certified status based on input fromother users in the form of a rating system or other peer-approvalprocess. Alternatively or additionally, a protocol can achieve certifiedstatus based upon one or more publications where the protocol is noted,followed and/or discussed by the authors. A protocol that has achievedcertified status may also encourage more users to use a particularprotocol.

As used herein, the term “sample preparation related data” refers toinformation related to a sample preparation procedure, includingexecutable instructions for carrying out a sample preparation procedureon a device, and/or data related to a specific sample preparationprocedure such as sample identification, date, time and other particulardetails of sample preparation procedure. For example, sample preparationrelated data can include sample preparation recipe/protocolidentification, sample preparation cartridge identification, cartridgepreparation identification, sample preparation instrumentidentification, and other parameters. In some embodiments, samplepreparation related data is input or provided by a user to a samplepreparation device. In some embodiments, sample preparation related datais provided by a user to a third party, or to a cloud computingenvironment. In some embodiments, sample preparation related data isprovided from a cloud computing environment or a third party to a samplepreparation device.

As used herein, the term “sequencing related data” refers to informationprovided in connection with sequencing. For example, sequencing relateddata can include, but is not limited to, flowcell identification,sequencing cartridge identification, sequencing instrumentidentification, and sequencing parameters. Sequencing related data canbe provided, for example, by a user, a third party, or by a sequencinginstrument. In some embodiments, sequencing related data is input orprovided by a user to a sample preparation device. In some embodiments,sequencing related data is provided by a user to a third party, or to acloud computing environment. In some embodiments, sequencing relateddata is provided from a cloud computing environment or a third party toa sample preparation device.

As used herein, the term “crowd-sourced” refers to computing resourcesallocated to particular projects or users within the cloud computingenvironment. One example of crowdsourcing in the methods provided hereinincludes analysis (e.g., primary, secondary, and/or tertiary analysis)of sequencing data. Another example includes the reporting and/orannotation of sequencing data.

As used herein, the term “sample extraction related data” refers toinformation provided in connection with sample extraction. For example,sample extraction related data can include, but is not limited to,parameters and/or executable instructions for sample extraction from abiological source. Other examples of sample extraction related datainclude sample identification, sample plate identification, and plateposition identification.

As used herein, the term “sample manifest” refers to a list includingone or more of the samples being processed in a sample preparationprocedure. The sample manifest may include, for example, identifiernumbers or other identifying information for the one or more samples. Insome embodiments, the samples on the sample manifest are processed inparallel. In some embodiments, the samples on the sample manifest areprocessed consecutively.

As used herein, the term “flowcell” refers to a chamber comprising asolid surface across which one or more fluid reagents can be flowed. Insome embodiments, one or more steps of sample preparation take place ina flowcell. In some embodiments, one or more steps of sequencing takeplace in a flowcell. Examples of flowcells and related fluidic systemsand detection platforms that can be readily used in the methods of thepresent disclosure are described, for example, in Bentley et al., Nature456:53-59 (2008), WO 04/018497; U.S. Pat. No. 7,057,026; WO 91/06678; WO07/123,744; U.S. Pat. No. 7,329,492; U.S. Pat. No. 7,211,414; U.S. Pat.No. 7,315,019; U.S. Pat. No. 7,405,281, and US 2008/0108082, each ofwhich is incorporated herein by reference.

Turning now to the drawings, and referring first to FIG. 1, a cloudcomputing environment 10 for biological data and/or related informationis illustrated diagrammatically. As used herein, the term “cloud” or“cloud computing environment” may refer to various evolvingarrangements, infrastructure, networks, and the like that will typicallybe based upon the Internet. The term may refer to any type of cloud,including client clouds, application clouds, platform clouds,infrastructure clouds, server clouds, and so forth. As will beappreciated by those skilled in the art, such arrangements willgenerally allow for use by owners or users of sequencing devices,provide software as a service (SaaS), provide various aspects ofcomputing platforms as a service (Paas), provide various networkinfrastructures as a service (IaaS) and so forth. Moreover, included inthis term should be various types and business arrangements for theseproducts and services, including public clouds, community clouds, hybridclouds, and private clouds. Any or all of these may be serviced by thirdparty entities. However, in certain embodiments, private clouds orhybrid clouds may allow for sharing of sequence data and services amongauthorized users.

The cloud computing environment 12 includes a plurality of distributednodes 14. The computing resources of the nodes 14 are pooled to servemultiple consumers, with different physical and virtual resourcesdynamically assigned and reassigned according to consumer demand.Examples of resources include storage, processing, memory, networkbandwidth, and virtual machines. The nodes 14 may communicate with oneanother to distribute resources, and such communication and managementof distribution of resources may be controlled by a cloud managementmodule 15, residing on one or more nodes 14. The nodes 14 maycommunicate via any suitable arrangement and protocol. Further, thenodes 14 may include servers associated with one or more providers. Forexample, certain programs or software platforms may be accessed via aset of nodes 14 provided by the owner of the programs while other nodes14 are provided by data storage companies. Certain nodes 14 may also beoverflow nodes that are used during higher load times.

In one embodiment, the cloud management module 15 is responsible forload management and cloud resources. The load management may beimplemented through consideration of a variety of factors, includinguser access level and/or total load in the cloud computing environment12 (peak times versus average load times). The project type may also beconsidered. In one embodiment, public health emergencies may beprioritized over other types of projects. Further, a user may managecosts by offering certain runs as lower priority that are held untilcloud usage is below a certain threshold.

The cloud computing environment 12 is configured to communicate withvarious users, including users of devices for generating biologicaldata. Such data may include sequence data generated via a device 16(e.g., sequencing device), which in particular embodiments may include adevice 18 that includes a module to accept a biological sample andgenerate sequence data and an associated computer 20 that includesexecutable instructions for analyzing or communicating the sequence datato the cloud computing environment 12. Alternatively or additionally,such data may include sample preparation data (e.g., library) generatedvia a device 36 (e.g., sample preparation device), which in particularembodiments may include a device 38 that includes a module to accept abiological sample and generate sample preparation data (e.g., library)and an associated computer 40 that includes executable instructions foranalyzing or communicating the sample preparation data to the cloudcomputing environment 12. It should be understood that, in certainembodiments, the devices 16 and 36 may be incorporated into a singledevice. The devices 16, 36 are configured to communicate with the cloudcomputing environment 12 via a suitable communications link 24, 42. Thecommunication with the cloud computing environment 12 may includecommunication via a local area network (LAN), a general wide areanetwork (WAN), and/or a public network (e.g., the Internet) via thecommunications link 24, 42. In particular, the communications link 24,42 sends sample preparation and/or sequence data 26 and, in certainembodiments, authentication information 28, to the cloud computingenvironment 12. The authentication information may confirm that thedevice 16, 36 is a client of the cloud computing environment 12.

As noted, the cloud computing environment 12 may serve multiple users orclients with associated devices, e.g., devices 16 a, 16 b, 16 c, 36 a,36 b, and 36 c. Further, the cloud computing environment 12 may also beaccessed by other types of clients, such as secondary users 30 or thirdparty software holders 34. Accordingly, the cloud computing environment12 may provide different types of services depending on the access levelof the particular client. A sequencing client may have access to storageand data analysis services, while a secondary user 30 may have accessonly to shared or public sequences. Third party software holders 34 maynegotiate with sequencing clients to determine appropriate accessprivileges. For example, open source software may be offered for free oron limited license basis, while other types of software may be offeredaccording to various fee or subscription bases. In certain embodiments,a supplier may support the cloud computing environment, and customers ofthe supplier may be given access to the cloud computing environment. Forexample, a purchase of a generic sample preparation cartridge from thesupplier of the generic sample preparation cartridge may enable a useraccess to sample preparation protocols and/or corresponding analysismethods on the cloud computing environment.

FIG. 2 is a schematic diagram of an implementation of an individual node14 of the cloud computing environment 12. The node 14 may be implementedas one or more of a personal computer system, server computer system,thin client, thick client, hand-held or laptop device, multiprocessorsystem, microprocessor-based system, set top box, programmable consumerelectronic, network PC, minicomputer system, mainframe computer system,and distributed cloud computing environments 12 that include any of theabove systems or devices, and the like. The node 14 may include one ormore processors or processing units 50, a memory architecture 52 thatmay include RAM 54 and non-volatile memory 56. The memory architecture52 may further include removable/non-removable, volatile/non-volatilecomputer system storage media. Further, the memory architecture 52 mayinclude one or more readers for reading from and writing to anon-removable, non-volatile magnetic media, such as a hard drive, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and/or an opticaldisk drive for reading from or writing to a removable, non-volatileoptical disk such as a CD-ROM, DVD-ROM. The node 14 may also include avariety of computer system readable media. Such media may be anyavailable media that is accessible by the cloud computing environment,such as volatile and non-volatile media, and removable and non-removablemedia.

The memory architecture 52 may include at least one program producthaving a set (e.g., at least one) of program modules implemented asexecutable instructions that are configured to carry out the functionsof the present techniques. For example, executable instructions 58 mayinclude an operating system, one or more application programs, otherprogram modules, and program data. Generally, program modules mayinclude routines, programs, objects, components, logic, data structures,and so on, that perform particular tasks or implement particularabstract data types. Program modules may carry out the functions and/ormethodologies of the techniques as described herein including, but notlimited to, library generation, primary sequence data analysis,secondary sequence analysis, tertiary sequence analysis, and reporting.

The components of the node 14 may be coupled by an internal bus 60 thatmay be implemented as one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

The node 14 may also communicate with one or more external devices suchas a keyboard, a pointing device, a display 62, etc.; that enable a userto interact with the cloud computing environment 12; and/or any devices(e.g., network card, modem, etc.) that enable node 14 to communicatewith one or more other computing devices. Such communication can occurvia I/O interfaces 64. Still yet, the nodes 14 of the cloud computingenvironment 12 may communicate with one or more networks such as a localarea network (LAN), a general wide area network (WAN), and/or a publicnetwork (e.g., the Internet) via a suitable network adapter.

FIG. 3 is a schematic diagram of the sequencing device 16 that may beused in conjunction with the cloud computing environment 12. Thesequence device 16 may be implemented according to any sequencingtechnique, such as those incorporating sequencing-by-synthesis methodsdescribed in U.S. Patent Publication Nos. 2007/0166705; 2006/0188901;2006/0240439; 2006/0281109; 2005/0100900; U.S. Pat. No. 7,057,026; WO05/065814; WO 06/064199; WO 07/010,251, the disclosures of which areincorporated herein by reference in their entireties. Alternatively,sequencing by ligation techniques may be used in the sequencing device16. Such techniques use DNA ligase to incorporate oligonucleotides andidentify the incorporation of such oligonucleotides and are described inU.S. Pat. No. 6,969,488; U.S. Pat. No. 6,172,218; and U.S. Pat. No.6,306,597; the disclosures of which are incorporated herein by referencein their entireties. Some embodiments can utilize nanopore sequencing,whereby target nucleic acid strands, or nucleotides exonucleolyticallyremoved from target nucleic acids, pass through a nanopore. As thetarget nucleic acids or nucleotides pass through the nanopore, each typeof base can be identified by measuring fluctuations in the electricalconductance of the pore (U.S. Pat. No. 7,001,792; Soni & Meller, Clin.Chem. 53, 1996-2001 (2007); Healy, Nanomed. 2, 459-481 (2007); andCockroft, et al. J. Am. Chem. Soc. 130, 818-820 (2008), the disclosuresof which are incorporated herein by reference in their entireties). Yetother embodiments include detection of a proton released uponincorporation of a nucleotide into an extension product. For example,sequencing based on detection of released protons can use an electricaldetector and associated techniques that are commercially available fromIon Torrent (Guilford, Conn., a Life Technologies subsidiary) orsequencing methods and systems described in US 2009/0026082 A1; US2009/0127589 A1; US 2010/0137143 A1; or US 2010/0282617 A1, each ofwhich is incorporated herein by reference in its entirety. Particularembodiments can utilize methods involving the real-time monitoring ofDNA polymerase activity. Nucleotide incorporations can be detectedthrough fluorescence resonance energy transfer (FRET) interactionsbetween a fluorophore-bearing polymerase and γ-phosphate-labelednucleotides, or with zeromode waveguides as described, for example, inLevene et al. Science 299, 682-686 (2003); Lundquist et al. Opt. Lett.33, 1026-1028 (2008); Korlach et al. Proc. Natl. Acad. Sci. USA 105,1176-1181 (2008), the disclosures of which are incorporated herein byreference in their entireties. Other suitable alternative techniquesinclude, for example, fluorescent in situ sequencing (FISSEQ), andMassively Parallel Signature Sequencing (MPSS). In particularembodiments, the sequencing device 16 may be a HiSeq, MiSeq, or HiScanSQfrom Illumina (San Diego, Calif.).

In the depicted embodiment, the sequencing device 16 includes a separatesample processing device 18 and an associated computer 20. However, asnoted, these may be implemented as a single device. Further, theassociated computer 20 may be local to or networked with the sampleprocessing device 18. In other embodiments, the computer 20 may includea cloud computing environment access device that is remote from thesequencing device 16. That is, the computer 20 may be capable ofcommunicating with the sequencing device 16 through the cloud computingenvironment 12. In the depicted embodiment, the biological sample may beloaded into the sample processing device 18 as a sample slide 70 that isimaged to generate sequence data. For example, reagents that interactwith the biological sample fluoresce at particular wavelengths inresponse to an excitation beam generated by an imaging module 72 andthereby return radiation for imaging. For instance, the fluorescentcomponents may be generated by fluorescently tagged nucleic acids thathybridize to complementary molecules of the components or tofluorescently tagged nucleotides that are incorporated into anoligonucleotide using a polymerase. As will be appreciated by thoseskilled in the art, the wavelength at which the dyes of the sample areexcited and the wavelength at which they fluoresce will depend upon theabsorption and emission spectra of the specific dyes. Such returnedradiation may propagate back through the directing optics 26. Thisretrobeam may generally be directed toward detection optics of theimaging module 72.

The imaging module detection optics may be based upon any suitabletechnology, and may be, for example, a charged coupled device (CCD)sensor that generates pixilated image data based upon photons impactinglocations in the device. However, it will be understood that any of avariety of other detectors may also be used including, but not limitedto, a detector array configured for time delay integration (TDI)operation, a complementary metal oxide semiconductor (CMOS) detector, anavalanche photodiode (APD) detector, a Geiger-mode photon counter, orany other suitable detector. TDI mode detection can be coupled with linescanning as described in U.S. Pat. No. 7,329,860, which is incorporatedherein by reference. Other useful detectors are described, for example,in the references provided previously herein in the context of variousnucleic acid sequencing methodologies.

The imaging module 72 may be under processor control, e.g., via aprocessor 74, and the sample receiving device 18 may also include I/Ocontrols 76, an internal bus 78, non-volatile memory 80, RAM 82 and anyother memory structure such that the memory is capable of storingexecutable instructions, and other suitable hardware components that maybe similar to those described with regard to FIG. 2. Further, theassociated computer 20 may also include a processor 84, I/O controls 86,a communications module 87, and a memory architecture including RAM 88and non-volatile memory 90, such that the memory architecture is capableof storing executable instructions 92. The hardware components may belinked by an internal bus 94, which may also link to the display 95. Inembodiments in which the sequencing device is implemented as anall-in-one device, certain redundant hardware elements may beeliminated.

Further, a primary user (or secondary user) may also interact with thecloud computing environment 12 through any appropriate access device,such as a general purpose computer or mobile device that includescomponents similar to those described with regard to the computer 20.That is, once the sequence data has been communicated to the cloudcomputing environment 12, further interaction with and access to thesequence data may not necessarily be coupled to the sequence device 16.Such embodiments may be beneficial in embodiments in which the owner ofthe biological sample and/or sequence data has contracted forsequencing, e.g., to a core laboratory facility. In such embodiments,the primary user may be the owner while the core laboratory facilityassociated with the sequencing device 16 is at most a secondary userafter the sequence data has been communicated to the cloud computingenvironment 12. In certain embodiments, the sequence data may beaccessed through security parameters such as a password-protected clientaccount in the cloud computing environment 12 or association with aparticular institution or IP address. The sequence data may be accessedby downloading one or more files from the cloud computing environment 12or by logging into a web-based interface or software program thatprovides a graphical user display in which the sequence data is depictedas text, images, and/or hyperlinks. In such an embodiment, the sequencedata may be provided to the primary or secondary user in the form ofdata packets transmitted via a communications link or network.

The cloud computing environment 12 may execute user interaction software(e.g., via a web-based interface or application platform) that providesa graphical user interface for users and that facilitates access tosequence data, a community or group of researchers, data analysisprograms, available third party software, and user selections for loadbalancing and instrument settings. For example, in particularembodiments, settings for a sequencing run on a sequencing device 16 maybe set via the cloud computing environment 12. Accordingly, the cloudcomputing environment 12 and an individual sequencing device 16 may becapable of two-way communication. Such an embodiment may be particularlyuseful for controlling parameters of a remote sequencing run.

FIG. 4 is a schematic diagram of the sample preparation device 36 thatmay be used in conjunction with the cloud computing environment 12. Thesample preparation device 36 may be implemented according to customizeduser derived protocols in an automated manner. In particularembodiments, the sample preparation device 36 may be a cBOT clustergeneration device or the cluster generation component of a MiSeqsequencing device (CBOT and MiSeq available from Illumina, San Diego,Calif.).

In the depicted embodiment, the sample preparation device 36 includes aseparate sample processing device 38 and an associated computer 40.However, these may be implemented as a single device. Further, theassociated computer 20 may be local to or networked with the sampleprocessing device 38. In other embodiments, the computer 40 may includea cloud computing environment access device that is remote from thesample preparation device 36. That is, the computer 40 may be capable ofcommunicating with the sample preparation device 36 through the cloudcomputing environment 12. In the depicted embodiment, the biologicalsample may be loaded into the device 38 via a sample preparationcartridge 96. The sample preparation cartridge 96 can be utilized toconvert nucleic acid samples (e.g., DNA, RNA) into libraries for use insequencing (e.g., massive parallel sequencing).

A sample preparation cartridge 96 can be a specific cartridge that isconfigured for use with a particular protocol or, alternatively, it canbe a generic cartridge capable of being used for a variety of differentprotocols. For example a specific cartridge 96 may include specificcompartments and connections required for and dedicated to a specificapplication (e.g., whole transcriptome sample preparation). In contrast,a generic cartridge can include compartments, channels or other fluidicfeatures that are greater in number or more variable in configurationthan necessary for any single specific application of the cartridge. Theuse of the generic cartridge 96 enables a user to utilize a customizedprotocol for use with the cartridge 96 to address the specific need orapplication of the user. In addition, the use of the generic cartridge96 may encourage users to utilize automated sample preparation which mayresult in a cost savings in reagents, while providing higher precisionand reproducibility in preparing samples (e.g., libraries) forsequencing.

A sample preparation cartridge 96, whether specific or generic inconfiguration, need not include any reagents. Rather the cartridge canbe supplied to a user empty and the user can subsequently load thecartridge with desired reagents or fluidic components. In particularembodiments, the generic cartridge 96 may be designed for use with adigital microfluidics based system. Exemplary devices and procedures fordigital microfluidics are set forth for example in, PCT ApplicationSerial No. PCT/US12/63741, U.S. Pat. Nos. 6,911,132; 8,048,628 and6,773,566; and U.S. Patent Pub. Nos. 2005/0179746 A1; 2010/0236928 and2011/0311980, each of which is incorporated herein by reference in itsentirety. Digital microfluidics systems move fluid droplets alongdynamic pads by alternating the hydrophilicity and hydrophobicity of thepads. Pads that are in a hydrophilic state attract aqueous droplets andpads that are in a hydrophobic state repel droplets. Thus, droplets canbe moved, mixed, split and otherwise manipulated by a schedule ofhydrophobic/hydrophilic alternations for a set of dynamic pads thatinteract with the droplets. Digital microfluidic devices areparticularly useful for a generic cartridge because a grid of dynamicpads can be programmed in different ways to carry out different samplepreparation protocols. The programming can be specified by any of avariety of communication routes set forth herein, including for example,a route from or within a cloud computing environment.

Further, the associated computer 40 may also include a processor 98, I/Ocontrols 100, a communications module 102, and a memory architectureincluding RAM 104 and non-volatile memory 106, such that the memoryarchitecture is capable of storing executable instructions 108. Thehardware components may be linked by an internal bus 110, which may alsolink to the display 112. In embodiments in which the sample preparation36 device is implemented as an all-in-one device, certain redundanthardware elements may be eliminated.

Further, a primary user (or secondary user) may also interact with thecloud computing environment 12 through any appropriate access device,such as a general purpose computer or mobile device that includescomponents similar to those described with regard to the computer 40.That is, once the sequence data has been communicated to the cloudcomputing environment 12, further interaction with and access to thesample preparation data may not necessarily be coupled to the samplepreparation device 36. Such embodiments may be beneficial in embodimentsin which the owner of the biological sample and/or sample preparationdata has contracted for sample preparation, e.g., to a core laboratoryfacility. In such embodiments, the primary user may be the owner whilethe core laboratory facility associated with the sample preparationdevice 36 is at most a secondary user after the sequence data has beencommunicated to the cloud computing environment 12. In certainembodiments, the sample preparation data may be accessed throughsecurity parameters such as a password-protected client account in thecloud computing environment 12 or association with a particularinstitution or IP address. The sample preparation data may be accessedby downloading one or more files from the cloud computing environment 12or by logging into a web-based interface or software program thatprovides a graphical user display in which the sample preparation datais depicted as text, images, and/or hyperlinks. In such an embodiment,the sample preparation data may be provided to the primary or secondaryuser in the form of data packets transmitted via a communications linkor network.

The cloud computing environment 12 may execute user interaction software(e.g., via a web-based interface or application platform) that providesa graphical user interface for users and that facilitates access tosample preparation data, a community or group of researchers, dataanalysis programs, available third party software, and user selectionsfor load balancing and instrument settings. For example, in particularembodiments, settings (i.e., protocol) for a sample preparation run onthe sample preparation device 36 may be set via the cloud computingenvironment 12. Accordingly, the cloud computing environment 12 and anindividual sample preparation device 36 may be capable of two-waycommunication. Such an embodiment may be particularly useful forcontrolling parameters of a remote sample preparation run.

As provided herein, the system 10 facilitates the sharing of samplepreparation protocols and the monitoring of the popularity of theseprotocols via the cloud computing environment 12. To that end, FIG. 5 isa schematic diagram of an exemplary system for sharing and monitoringthe popularity of sample preparation protocols. The depicted cloudcomputing environment 12 is as described above. In certain embodiments,the cloud computing environment 12 may be supported by a supplier (e.g.,manufacturer or provider) of the generic sample preparation cartridge 96(e.g., application developer cartridge) as described above for use withautomated sample preparation devices or instruments 36. In addition, thesupplier may also provide the sample preparation instrument 36. Adeveloper (e.g., submitter/consumer/user) uploads a customized andoptimized sample preparation protocol for use with the generic samplepreparation cartridge 96 to the cloud computing environment 12 asindicated by arrow 114. The upload of the sample preparation protocolmay be free to encourage sharing. The protocol is used to drive thesample preparation instrument 36 to perform specific steps for samplepreparation. For example, the steps may include mixing, incubation, andsplitting of the samples and/or reagents, among other steps. Inaddition, the protocol may specify a pre-determined amount of timeand/or a temperature for each step. For example, in the case of adigital microfluidics device, the protocol can specify a schedule foractuating dynamic pads that lead to movement, splitting and/or mixing ofdroplets to prepare a sample for a particular analytical procedure (e.g.preparation of a nucleic acid library for nucleotide sequencing). Incertain embodiments, the developer may also upload a correspondinganalysis method for use with the uploaded sample preparation protocol.

The cloud computing environment 12 (e.g., memory) stores a number ofdeveloper-submitted protocols 116 for access by users (e.g.,requesters/customers). These optimized protocols 116 may encourage usersto use them because the users do not need waste time and resourcesdeveloping all of the steps for a particular application. Users may begranted access to the cloud computing environment 12 and the protocols116 via paying a fee to the supplier or purchasing a product (e.g.,generic sample preparation cartridge 96) from the supplier. In certainembodiments, access to the protocols 116 may be limited to those userswho purchase the generic sample preparation cartridge 96. Users withaccess to the protocols request and download (e.g., directly to thesample preparation instrument 36) a particular protocol 116 for use withthe generic sample preparation cartridge 96 as represented by arrow 118.

The cloud computing environment 12 can monitor the usage of each of theprotocols (e.g. developer submitted, certified, supplier-supported). Forexample, the cloud computing environment 12 monitors the number ofrequests or downloads 120 for each protocol to determine popularity ofthe protocol or to evaluate more specific causes for increased use ofthe protocol (e.g. an outbreak of a particular pathogen that isdetectable using the protocol). In certain embodiments, the cloudcomputing environment 12 monitors the number of uses for each protocol.In addition, the cloud computing environment 12 receives and storesratings 122 from users of the protocols 116. Further, the cloudcomputing environment 12 may monitor publications for citations and/oruses of the developer-submitted protocols 116 in publications asrepresented by reference numeral 124. In addition, or alternatively, thecloud computing environment 12 may receive the publication citationsfrom the developer, user, and/or supplier. In either event thepublication citations and/or relevant information from the publicationscan be made available to individuals or devices that access the cloud.In particular embodiments, the availability of the protocols 116 citedin publications on the cloud computing environment enables users toaccess the protocols 116 directly without needing to look throughmultiple publications to find materials and methods and without needingto manually create a device protocol from a written description. Forcertain protocols, the supplier of the generic sample preparationcartridge 96 may perform independent validation, as represented byreference numeral 126, of the submitted protocol 116 and/orcorresponding analysis method.

Based on a combination of the ratings, citations in publications 124,and/or supplier validation, particular developer-submitted protocols 116and/or corresponding analysis methods may be conferred with a certifiedstatus as represented by arrow 128 to become certified samplepreparation protocols 130 and/or corresponding analysis methods. Thecertified status of the protocols 130 may encourage more users to use aparticular protocol 130 as represented by arrows 132. In turn, moreusers may be encouraged to obtain generic sample preparation cartridges96 and/or sample preparation instruments 36, e.g., from the supplier.The certified status may be determined by the supplier based oninformation obtained from the cloud computing environment 12.Alternatively, the cloud computing environment 12 (e.g., processor) maydetermine whether to confer the certified status based on executableinstructions or criteria provided to the cloud computing environment 12.

The supplier via the cloud computing environment 12 monitors thepopularity (e.g., number and frequency of requests and/or uses 120) forall of the protocols. A supplier may identify a niche application withmarket potential from among the certified protocols 130 (e.g., samplepreparation recipes and/or corresponding analysis methods). Uponidentifying such a niche application, the supplier via the cloudcomputing environment 12 may confer a supplier-supported status asrepresented by arrow 134 on the certified protocols 130 and/orcorresponding analysis methods to provide supplier-supported protocols136. In addition, the supplier may generate, design, or commercialize anapplication-specific sample preparation cartridge 138 (e.g., pre-filledwith reagents) based on the supplier-supported protocol 136. The user ofthe application-specific cartridge 138 may download or retrieve thesupplier-supported protocol 136 for use with the cartridge 138 asrepresented by arrows 140. The supplier-supported protocols 136 may alsoencourage even more users to obtain application-specific samplepreparation cartridges 138, sample preparation instruments 36, and/orrelated consumables, e.g., from the supplier.

To encourage sharing of protocols, the supplier via the cloud computingenvironment may provide credit to the submitter or developer of theprotocol 116 submitted to the cloud computing environment 12 for eachuser request for the submitted protocol 116. This credit may be used forpurchasing consumables (e.g., cartridges or fluid components), devices(e.g. sample preparation or sequencing devices) or services (e.g. customdata analysis, medical diagnosis or alternative sample analysis) fromthe supplier.

As discussed above, the system 10 facilitates interaction betweenprotocol developers, requesters, the supplier, and the cloud computingenvironment 12 in terms of sharing and monitoring the popularity ofsample preparation protocols. To that end, FIG. 6 is a flow chart of amethod 142 of some exemplary interactions for sharing and monitoring thepopularity of sample preparation protocols via the cloud computingenvironment 12. The method 142 may encompass any viable subset,combination, or modification of the steps or interactions depicted. Inone embodiment, the method 142 may begin with the submitter (e.g.,developer) optimizing a protocol for an assay that uses a supplier's(e.g., manufacturer's) generic sample preparation cartridge 96 with thesample preparation instrument 36 (block 144). The submitter uploads theoptimized sample preparation protocol and/or corresponding analysismethod to the supplier-supported cloud computing environment 12 (block146), for example, without charge. The cloud computing environment 12receives and stores the optimized protocol and/or corresponding analysismethod among other protocols and analysis methods (block 148).

The method 142 may include the requester (e.g., consumer) obtaining(e.g. by commercial purchase) the generic sample preparation cartridge96 from the supplier, in turn, giving the requester access to the cloudcomputing environment 12 (block 150). Upon receiving access to the cloudcomputing environment 12, the requester requests a particular protocoland/or corresponding analysis method from among the available protocolsand/or corresponding analysis methods (block 152). The cloud computingenvironment 12 receives the request for the particular protocol and/orcorresponding analysis method (block 154). The cloud computingenvironment 12 provides credit to the submitter of the requestedprotocol for the purchase consumables from the supplier (block 156),which the submitter of the requested protocol receives (block 158) foreach request and/or use of the protocol. The cloud computing environment12 also provides the requested protocol and/or corresponding analysismethod to the requester (block 160). Upon receiving the requestedprotocol and/or corresponding analysis method (block 162), the requesterperforms sample preparation with the generic sample preparationcartridge 96 using the requested protocol and/or corresponding analysismethod (block 164).

The method 142 may include the requester rating the protocol and/orcorresponding analysis method and providing the rating to the cloudcomputing environment (block 166). The cloud computing environment 12receives the rating for the protocol and/or corresponding analysismethod (block 168) from the requester as well as other requesters of theprotocol and/or corresponding analysis method. Additionally, the cloudcomputing environment receives one or more citations from publicationsthat cite and/or use the submitted protocol and/or correspondinganalysis method (block 170). The method 142 may also include thesupplier performing independent validation of the submitted protocoland/or corresponding analysis method (block 172). Based on a combinationof ratings, publication citations, and/or supplier validation of thesubmitted protocol and/or corresponding analysis method, the cloudcomputing environment 12 confers the certified status to the protocoland/or corresponding analysis method (block 174). As mentioned above,the certified status may be determined by the supplier based oninformation obtained from the cloud computing environment 12.Alternatively, the cloud computing environment 12 (e.g., processor) maydetermine whether to confer the certified status based on executableinstructions or criteria provided to the cloud computing environment 12.

The method 142 includes monitoring the number of requests or downloadsof the submitted protocol (pre- and post-certification) (block 176). Incertain embodiments, the method 142 may include monitoring the number ofuses of the requested or downloaded protocol. Based on the number ofrequests and/or the number of uses and other information (e.g., ratings,consumer demand for application, market considerations, etc.), thesupplier identifies if the certified protocol and/or correspondinganalysis method is commercializable (e.g., a niche application withmarket potential) (block 178). If the certified protocol is deemedcommercializable, the supplier develops and commercially provides theapplication-specific sample preparation cartridge based on the protocol(block 180). In addition, the method 142 includes conferring asupplier-supported status to the certified protocol and/or correspondinganalysis method on the cloud computing environment (block 182).

As mentioned above, in certain embodiments, the cloud computingenvironment 12 may be used to guide a genomic (e.g., sequencing)analysis workflow from beginning to end. Examples of cloud-guidedgenomic analysis workflows include, but are not limited to, whole genomesequencing, sample preparation for cancer sequencing, targetedresequencing, psedo-long read for whole genome haplotyping, and lowinput sample preparation (e.g., forensic purposes, single cell,virus-infected tissues). To that end, FIG. 7 a schematic overview of thecloud-based computing environment 12 to facilitate a cloud-guidedgenomic analysis workflow. In certain embodiments, the cloud computingenvironment 12 may be supported by a supplier (e.g.,manufacturer/provider) of products and/or instruments used in thegenomic analysis workflow. FIG. 7 depicts the major steps involved in atypical genomic (sequencing) analysis workflow. In certain embodiments,additional steps may be included or some steps not performed. Some ofthe steps (e.g., analysis and reporting steps) may be performed fromcomputing devices with access to the cloud computing environment 12. Ingeneral, upon gathering information (e.g., parameters) required for eachof the steps, the information is provided to the cloud computingenvironment 12 via computing devices or instruments. Certain sources ofthese parameters or information may include information from barcode- orRFID-tracked sample plates, sample preparation cartridges, flowcells,sequencing reagent cartridges, and other sources. In addition, variousmanifests and recipes (e.g., protocols) reside in the cloud computingenvironment 12 (e.g., memory). These manifests and recipes are providedto the instruments (e.g., sample preparation instrument 36, sequencinginstrument 18, etc.) to drive the specific steps (e.g., samplepreparation, sequencing, etc.). Upon beginning the specific tasks orsteps, data and instrument feedback is provided to cloud computingenvironment 12 for further steps (logging, analysis, report generation,annotation, etc.). The various analysis methods, report formats andannotation services also reside in the cloud computing environment 12.Also, the various sample preparation recipes (e.g., protocols), analysismethods, report formats, and annotation services may be developed by thesupplier or crowd-sourced (e.g., see FIGS. 5 and 6) as indicated byreference numeral 183. The steps of the workflow in the cloud computingenvironment 12 parallel the steps in the laboratory. This enables thecloud computing environment to act as a workflow manager (e.g., in anapplication-centric fashion) to guide the physical process from start tofinish.

Turning to FIG. 7, in one embodiment the workflow may begin with sampleextraction from a biological source. A sample manifest residing on thecloud computing environment 12 (e.g., provided by the user or anothersource) is provided to the user as represented by arrow 184. Upon and/orduring sample extraction, a user provides sample extraction related data(sample identification, sample plate identification, plate positionidentification, extraction yield, other parameters, etc.) to the cloudcomputing environment 12 via, e.g., a computing device as represented byarrow 184. Based on the sample extraction related data and/or samplemanifest, the cloud computing environment 12 (e.g., processor) generatesa sample extraction log.

After sample extraction, the workflow shifts to sample preparation asindicated by arrows 186, 188. The sample preparation device 36 or theuser (or third party) via a different computing device provides samplepreparation related data (e.g., sample preparation recipe/protocolidentification, sample preparation cartridge identification, cartridgepreparation identification, sample preparation instrumentidentification, other parameters, etc.) to the cloud computingenvironment 12 as represented by arrow 190. In turn, the cloud computingenvironment 12 provides a sample preparation recipe and samplepreparation manifest to the sample preparation instrument 36 to drivethe sample preparation as represented by arrow 190. In certainembodiments, the sample preparation by the sample preparation instrument36 may be automatically initiated from the cloud computing environment12. In some embodiments, the sample preparation protocol or recipe usedby the sample preparation instrument 36, via instructions from the cloudcomputing environment 12, may be based on a protocol selected by a user,a protocol selected or instructed by a third party, or a protocolautomatically loaded based on sample or cartridge identification. Uponand/or during sample preparation, sample preparation data is provided asshown by arrow 190 to the cloud computing environment 12. Based on thesample extraction log, sample preparation related data, samplepreparation data, sample preparation recipe, and/or sample preparationrecipe, the cloud computing environment 12 (e.g., processor) generates asample preparation log.

After sample preparation, the workflow shifts to sequencing as indicatedby arrows 192, 194. The sequencing instrument 18 or the user (or thirdparty) via a different computing device provides sequencing related data(e.g., flowcell identification, sequencing cartridge identification,sequencing instrument identification, other parameters, etc.) to thecloud computing environment 12 as represented by arrow 196. In turn, thecloud computing environment 12 provides instructions (e.g., sequencingprotocol) for performing sequencing via the sequencing instrument 18 asrepresented by arrow 196. In certain embodiments, the sequencing by thesequencing instrument 18 may be automatically initiated from the cloudcomputing environment 12. In some embodiments, the sequencing protocolused by the sequencing instrument 18, via instructions from the cloudcomputing environment 12, may be based on a protocol selected by a user,a protocol selected or instructed by a third party, or a protocolautomatically loaded based on the sequencing related data. Upon and/orduring sequencing, the sequencing instrument 18 provides sequencing datato the cloud computing environment 12. Based on the sample preparationlog, sequencing data, and/or sequencing related data, the cloudcomputing environment 12 (e.g., processor) generates run data and a runlog.

After sequencing, the workflow shifts to analysis as indicated by arrows198, 200. The sequencing instrument 18 or the user via a differentcomputing device provides analysis related data (e.g., post-analysisdata, analysis identification, other parameters, etc.) to the cloudcomputing environment 12 as represented by arrow 202. In turn, the cloudcomputing environment 12 may provide an analysis method to thesequencing instrument 18 or the user via a different computing device asrepresented by arrow 202. In certain embodiments, the analysis methodscan be hosted in BaseSpace from Illumina (San Diego, Calif.). In certainembodiments, the cloud computing environment 12 performs the analysis(e.g., primary, secondary, and/or tertiary analysis) using the analysismethod, the run data, and/or the run log. In some embodiments, thesequencing instrument 18 performs some of the analysis (e.g., primaryand/or secondary analysis). In other embodiments, a different computingdevice may perform the analysis (e.g., primary, secondary, and/ortertiary analysis). In certain embodiments, the analysis may becrowd-sourced 183. Based on the run data, run log, analysis relateddata, and/or analysis method, the cloud computing environment 12 (e.g.,processor) generates post-analysis data and an analysis log.

After analysis, the workflow shifts to reporting as indicated by arrows204, 206. The user via a different computing device provides reportingrelated data (e.g., report identification, share privileges, otherparameters, etc.) to the cloud computing environment 12 as representedby arrow 208. In turn, the cloud computing environment 12 may provide areport format and/or an annotation plug-in or service to the user on thecomputing device as represented by arrow 208. In certain embodiments,the cloud computing environment 12 performs the reporting and/orannotation using the post-analysis data, analysis log, report format,annotation plug-in, and/or reporting related data. In other embodiments,the user may perform the reporting and/or annotation on a differentcomputing device. In certain embodiments, the reporting and/orannotation may be crowd-sourced 183. Based on the post-analysis data,analysis log, reporting related data, report format, and/or annotationplug-in, the cloud computing environment 12 (e.g., processor) generatesan archived report.

As discussed above, the system 10 facilitates interaction between users(e.g., primary and secondary users), the supplier, and the cloudcomputing environment 12 to facilitate the genomic analysis workflow. Inparticular, the cloud computing environment 12 and information storedtherein serves as a workflow manager to guide the physical process fromstart to end in an application-centric fashion as the samples arephysically moved through the various steps of the genomic analysisworkflow. To that end, FIG. 8 is a flow chart of a method 208 of someexemplary interactions for a cloud-guided genomic analysis workflow. Themethod 208 may encompass any viable subset, combination, or modificationof the steps or interactions depicted. In addition, certain steps of themethod 208 performed by the user may be performed by distinct users(e.g., primary and secondary users). Further, certain steps of themethod 208 may be crowd-sourced.

In one embodiment, the method 208 may begin by a user extracting one ormore biological samples from a biological source (block 210). The userprovides to and/or receives from the cloud computing environment 12sample extraction related data (block 210), e.g., via a computingdevice. For example, the user may provide sample identification, sampleplate identification, plate position identification, or other parametersto the cloud computing environment 12 for storage (e.g., memory) and/orprocessing (e.g., processor). In turn, the cloud computing environment12 (e.g., server) provides sample extraction related data to the userand/or receives the sample preparation extraction related data (block214). For example, the cloud computing environment 12 may provide asample manifest or sample extraction log to the user. In certainembodiments, at least some of the sample extraction related data may beprovided to the user from the cloud computing environment 12 prior tosample extraction (block 210). Based on the sample extraction relateddata received from the user and/or the sample manifest from the cloudcomputing environment 12, the cloud computing environment 12 (e.g.,processor) generates the sample extraction log(block 216).

Following sample extraction, the method 208 includes conducting samplepreparation on the sample preparation instrument 36 (e.g., automatedsample preparation instrument) (block 218). The sample preparationinstrument 36 provides to and/or receives from the cloud computingenvironment 12 sample preparation related data (block 220). In certainembodiments, the user provides and/or receives the sample preparationrelated data via another computing device. For example, the samplepreparation instrument 36 may provide sample preparation recipeidentification, sample preparation cartridge identification, samplepreparation cartridge position identification, sample preparationinstrument identification, generated sample preparation data, and otherparameters to the cloud computing environment 12 for storage (e.g.,memory) and/or processing (e.g., processor). In certain embodiments, theinstrument 36 provides the generated sample preparation data to thecloud computing environment 12 during and/or after the generation of thedata. In turn, the cloud computing environment 12 (e.g., server)provides sample preparation related data to the sample preparationinstrument 36 and/or user and/or receives the sample preparation relateddata (block 222). For example, the cloud computing environment 12 mayprovide the sample extraction log, sample preparation recipe, samplepreparation manifest, and/or sample preparation log to the instrument 36and/or user. In certain embodiments, at least some of the samplepreparation related data may be provided to the instrument 36 prior tosample preparation (block 218). The sample preparation recipe and otherinformation may be used to drive the sample preparation instrument 36.Based on the sample extraction log, sample preparation related dataand/or generated sample preparation data received from the samplepreparation instrument 36 and/or cloud computing environment 12, thecloud computing environment 12 (e.g., processor) generates the samplepreparation log.

Following sample preparation, the method 208 includes generatingsequence data on the sequencing instrument 16 (block 226). Thesequencing instrument 16 provides to and/or receives from the cloudcomputing environment 12 sequencing related data (block 228). In certainembodiments, the user provides and/or receives the sequencing relateddata via another computing device. For example, the sequencinginstrument 16 may provide flowcell identification, sequencing cartridgeidentification, sequencing instrument identification, generated sequencedata, and other parameters to the cloud computing environment 12 forstorage (e.g., memory) and/or processing (e.g., processor). In certainembodiments, the instrument 16 provides the generated sequence data tothe cloud computing environment 12 during and/or after the generation ofthe data. In turn, the cloud computing environment 12 (e.g., server)receives the sequencing related data from the sequencing instrument 16and/or provides sequencing related data to the instrument 16 (block 230)and/or user. For example, the cloud computing environment 12 may providethe sample preparation log, task instructions, run data and/or a run logto the instrument 16 and/or user. In certain embodiments, at least someof the sequencing related data (e.g., task instructions) may be providedto the instrument 16 prior to sequencing (block 226). The taskinstructions and other information may be used to drive the sequencinginstrument 16. Based on the sample preparation log, sequencing relateddata, and/or generated sequence data received from the sequencinginstrument 16 and/or cloud computing environment 12, the cloud computingenvironment 12 (e.g., processor) generates the run log and/or run data(block 232).

Following sequencing, the method 208 includes analyzing the sequencedata (e.g., primary and/or secondary analysis) on the sequencinginstrument 16 (block 226). The sequencing instrument 16 provides and/orreceives from the cloud computing environment 12 analysis related data(block 236). In certain embodiments, the user provides and/or receivesthe analysis related data via another computing device. For example, thesequencing instrument 16 may provide analysis identification,post-analysis data, and/or other parameters to the cloud computingenvironment 12 for storage (e.g., memory) and/or processing (e.g.,processor). In certain embodiments, the instrument 16 provides thepost-analysis data to the cloud computing environment 12 during and/orafter the generation of the data. In turn, the cloud computingenvironment 12 (e.g., server) receives the post-analysis and analysisrelated data from the sequencing instrument and/or performs analysis(e.g., primary, secondary, and/or tertiary analysis) on the sequencingdata via at least one processor (block 238). For example, the cloudcomputing environment 12 may provide an analysis method to theinstrument 16 prior to analyzing the sequence data. As mentioned above,the analysis (e.g., primary, secondary, and/or tertiary analysis) of thesequencing data may be crowd-sourced. Based on the run data, run log,analysis related data, and/or analysis method received from thesequencing instrument 16 and/or cloud computing environment 12, thecloud computing environment 12 (e.g., processor) generates the analysislog and/or post-analysis data (block 240). The user receives theanalysis log and/or post-analysis data on another computing device(block 242).

Following analysis of the sequencing data, the method 208 includes theuser reporting and annotating the post-analysis data (block 244) viaanother computing device. In certain embodiments, the reporting andannotation of the post-analysis data may be crowed-sourced. The userprovides to and/or receives from the cloud computing environment 12reporting related data (block 246). For example, the user provides via acomputing device a report identification, share privilege information,and/or any reported and/or annotated data to the cloud computingenvironment 12 for storage (e.g., memory) and/or processing (e.g.,processor). In turn, the cloud computing environment 12 (e.g., server)provides reporting related data to the user and/or performs thereporting and analysis on the post-analysis data via at least oneprocessor (block 248). For example, the cloud computing environment 12may provide a report format, annotation plug-in or service, and/or anarchived report to the user. Based on the post-analysis data, analysislog, report format, annotation plug-in, and/or reporting related datafrom the user and/or cloud computing environment 12, the cloud computingenvironment 12 (e.g., processor) generates the archived report (block250).

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

1. A computer-implemented method for sharing and monitoring use ofprotocols for preparing biological samples using generic samplepreparation cartridges in a cloud computing environment, comprising:receiving from a submitter, at a server, a protocol for samplepreparation using a generic sample preparation cartridge on the cloudcomputing environment; monitoring for a request from a requester for theprotocol or for a use of the protocol; and crediting the submitter witha purchase credit from a supplier of the generic sample preparationcartridge for at least one request for the protocol or use of theprotocol.
 2. The method of claim 1, comprising receiving from therequester, at the server, a request for the protocol.
 3. The method ofclaim 2, comprising providing to the requester the protocol or theprotocol and an analysis method for use with the protocol.
 4. The methodof claim 3, comprising receiving a rating for the protocol from therequester.
 5. The method of claim 1, comprising receiving from thesubmitter, at the server, an analysis method for use with the protocol.6. The method of claim 1, comprising receiving from the supplier, at theserver, a validation of the protocol.
 7. The method of claim 1,comprising receiving, at the server, a citation of a publication thatcites, uses, or both cites and uses the protocol.
 8. The method of claim1, comprising conferring a certified status to the protocol based on anumber of requests for the protocol by requesters, ratings of theprotocol by the requesters, a number of citations for the protocol,validation of the protocol, or a combination thereof.
 9. The method ofclaim 8, comprising conferring a supplier supported status to theprotocol, wherein the protocol is utilized with an application-specificsample preparation cartridge designed by the supplier based on theprotocol.
 10. A system for sharing and monitoring use of protocols forpreparing biological samples using generic sample preparationcartridges, comprising: a cloud computing environment in communicationwith a plurality of computer systems, wherein the cloud computingenvironment comprises at least one server and at least one processor,the at least one server being configured to communicate with at leastone of the computer systems to receive and store a protocol for samplepreparation using a generic sample preparation cartridge, and the atleast one processor being configured to monitor for a request by arequester for the protocol and to credit a submitter of the protocolwith purchase credit from a supplier of the generic sample preparationcartridge for at least one request for the protocol.
 11. The system ofclaim 10, wherein the at least one server is configured to receive arequest for the protocol from the requester.
 12. The system of claim 11,wherein the at least one server is configured to provide to therequester the protocol or the protocol and an analysis method for usewith the protocol.
 13. The system of claim 13, wherein the at least oneserver is configured to receive a rating for the protocol from therequester.
 14. The system of claim 10, wherein the at least one serveris configured to receive from the submitter an analysis method for usewith the protocol.
 15. The system of claim 10, wherein the at least oneserver is configured to receive from the supplier a validation of theprotocol.
 16. The system of claim 10, wherein the at least one server isconfigured to receive a citation of a publication that cites, uses, orboth cites and uses the protocol.
 17. The system of claim 10, whereinthe at least one processor is configured to confer a certified status tothe protocol based on a number of requests for the protocol byrequesters, ratings of the protocol by the requesters, a number ofcitations for the protocol, or a combination thereof.
 18. The system ofclaim 17, wherein the at least one processor is configured to confer asupplier supported status to the protocol, wherein the protocol isutilized with an application-specific sample preparation cartridgedesigned by the supplier based on the protocol.
 19. The system of claim18, wherein the at least one server is configured to receiveinstructions from the supplier to confer the supplier supported statusto the protocol.
 20. A system for sharing and monitoring use ofprotocols for preparing biological samples using generic samplepreparation cartridges, comprising: a cloud-based server incommunication with a plurality of computer systems; a memory componentthat receives, via the server, protocols for sample preparation usinggeneric sample preparation cartridges and stores the protocols; and aprocessor configured to: receive requests for one or more of theprotocols; monitor a number of requests or uses for each of theprotocols; and credit a submitter of a respective protocol with apurchase credit from a supplier of the generic sample preparationcartridges for at least one request for the respective protocol or useof the respective protocol.